Kiyo

Model Details

Training Data

Benchmarks

Self-reported results from the official BananaMind Base Bench 1.1 script, all measured with the same runner, dtype (bfloat16) and GPU.

Figures for BananaMind-2-Pro, Rose-Pro, and GPT-2 are as self-reported on their own model cards, all against the same BananaMind Base Bench 1.1 suite.

Detailed Kiyo-135M result

* = passes 1.96σ vs. chance; n=50 per category

By difficulty

Summary

Scores are self-evaluated and may vary with the benchmark revision, Transformers version, dtype, hardware, and generation settings.

Usage

pip install -U transformers safetensors torch

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "DedeProGames/Kiyo-135M"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16,
).cuda().eval()

prompt = "The meaning of life is "
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)

with torch.no_grad():
    output = model.generate(
        input_ids=input_ids,
        max_new_tokens=64,
        do_sample=False,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

This is a base model, not instruction-tuned — it continues text rather than following instructions. At 135M parameters it produces fluent, well-structured text and is strong on language completion and code, but accuracy drops on quantitative and multi-step context-tracking tasks. It can generate incorrect facts and should not be used for high-stakes decisions without verification. Keep a finite generation limit to avoid repetition or drift on long outputs.

License

Apache 2.0